Low delay low complexity lossless compression system

ABSTRACT

A method for compressing data is disclosed. The method may include receiving data from one or more data sources. The method may also include selectively classifying the data into one or more data streams, the one or more data streams including at least PCM-encoded data and image data. The method may further include separately compressing the PCM-encoded data and the image data into first and second compressed bit-streams. The method may also include shuffling the first and second compressed bit-streams.

PRIORITY

This application is based on and claims priority to U.S. ProvisionalApplication No. 61/664,530, filed on Jun. 26, 2012 and U.S. ProvisionalApplication No. 61/665,053, filed on Jun. 27, 2012. The disclosure ofeach of these provisional applications is incorporated herein byreference.

GOVERNMENT RIGHTS

This invention was made with government support under the terms ofContract No. HQ0147-13-C-7308 and Contract No. HQ0147-13-C-7024 awardedby the Missile Defense Agency (MDA). The government may have certainrights to this invention.

TECHNICAL FIELD

The present disclosure relates generally to methods and systems for datacompression and data encoding and more particularly, to methods andsystems for data compression and data encoding of telemetry datastreams.

BACKGROUND

Telemetry is the measurement and transmission of data that is oftencollected at remote or inaccessible locations and transmitted to localsites, often for the purpose of real-time monitoring. Telemetry is usedin various fields including, for example, space exploration, oildrilling, flight testing, missile testing, and meteorology. In manysituations, the presence of a human observer at the point of datacollection is not feasible, but real-time access to the data foranalysis and decision-making may be necessary.

The extent to which data can be provided at a sufficient rate forreal-time applications depends in part on how much data can betransmitted in a given bandwidth of the telemetry system. Datacompression is one way in which the amount of data transmitted in aparticular time interval can be increased. The principle of datacompression is to reduce redundancy in a data set by efficientrepresentation of intrinsic correlation in the data. Compressiontechnologies can be divided into two categories: lossless and lossy.Lossless compression allows the exact original data to be reconstructedfrom the compressed data, while lossy compression cannot reconstructdata identical to the original, although a substantial amount of datacan remain to satisfy the need of a given application.

When compressing data, the higher the compression ratio, the higher thepotential rate of data transfer. However, data compression usuallyinvolves a trade-off between high compression ratio and resources (e.g.,time, computing power, and power consumption). In real-timeapplications, in which the time between generation of data and theprocessing and receipt of the data should be as small as possible,computation complexity and delay costs often cannot be tolerated.Further limitations are imposed when lossless compression is desired.Lossless compression usually achieves a lower compression ratio ascompared to lossy compression. Nevertheless, a low-delay,low-complexity, but high-ratio lossless compression of data is sometimesdesirable for some real-time telemetry systems. Known compressionalgorithms, such as the Lempel-Ziv algorithm or Huffman coding, can beused to compress telemetry data, but these compression algorithms maynot be able to reach sufficiently high compression ratios for the data.Furthermore, their computational complexity and delay costs may not betolerable in real-time applications.

The disclosed systems and methods are directed to overcoming one or moreof the shortcomings set forth above and/or other shortcomings of theprior art.

SUMMARY

In one aspect, the present disclosure is directed to a method forcompressing data. The method may include receiving data from one or moredata sources. The method may also include selectively classifying thedata into one or more data streams, the one or more data streamsincluding at least PCM-encoded data and image data. The method mayfurther include separately compressing the PCM-encoded data and theimage data into first and second compressed bit-streams. The method mayalso include shuffling the first and second compressed bit-streams.

In another aspect, the present disclosure is directed to a datatransmission system. The system may include a local unit comprising areceiver. The system may also include a remote unit, the remote unitcomprising transducers for generating data, a processor, and atransmitter. The processor may be configured to compress the data byclassifying the data from the transducers into classifications includingat least PCM-encoded data and image data. The processor may alsoseparately compress the PCM-encoded data and the image data into firstand second distinct compressed bit streams. The processor may furthershuffle each of the first and second compressed bit-streams to introducerandomness to the respective compressed bit-stream. The transmitter maytransmit the first and second compressed bit-streams to the local unit.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an exemplary disclosed telemetry system;

FIG. 2 is a block diagram of some aspects of this exemplary disclosedtelemetry system of FIG. 1;

FIG. 3 is a block diagram of an exemplary low-delay low-complexitylossless PCM compression encoder of FIG. 2 for linear PCM data;

FIG. 4 is a block diagram of an exemplary low-delay low-complexitylossless PCM compression encoder of FIG. 2 for logarithmic PCM data;

FIG. 5 illustrates a block diagram of an exemplary image compressionscheme of FIG. 2;

FIG. 6 illustrates a block diagram of an exemplary image compressionencoder of FIG. 2 for compressing video-type image data;

FIG. 7 illustrates a block diagram of an exemplary intra-frameprediction model of FIG. 6;

FIG. 8 illustrates a block diagram of an exemplary inter-frameprediction model of FIG. 6;

FIG. 9A illustrates a state machine of an exemplary multistate losslesscompression scheme;

FIG. 9B illustrates a block diagram of an exemplary multi-state,low-delay, low-compression, lossless compressor;

FIG. 10 illustrates a flowchart for an exemplary method of multi-statelossless compression;

FIG. 11 illustrates a flowchart for an exemplary method ofclassification of a segment of telemetry data performed by themulti-state, low-delay, low-compression, lossless compressor of FIG. 9B;

FIG. 12 illustrates a block diagram for an exemplary adaptive waveformencoder of FIG. 9B;

FIG. 13 illustrates a flowchart for an exemplary method of extracting atheme skeleton from a segment of telemetry data as performed by theadaptive waveform encoder of FIG. 12;

FIG. 14 illustrates a flowchart for an exemplary method of processing adata segment that is in a periodic state as performed by the adaptivewaveform encoder of FIG. 12;

FIG. 15 illustrates a flowchart for an exemplary method of processing adata segment that is in a slow-varying state as performed by theadaptive waveform encoder of FIG. 12;

FIG. 16 illustrates a flowchart for an exemplary method of processing adata segment that is in a transition state as performed by the adaptivewaveform encoder of FIG. 12;

FIG. 17 illustrates a flowchart for an exemplary method of processing adata segment that is noise as performed by noise encoder of FIG. 9B;

FIG. 18 shows a block diagram of an exemplary bit-stream shuffler ofFIG. 2; and

FIG. 19 shows a block diagram of another exemplary bit-stream shufflerof FIG. 2.

DETAILED DESCRIPTION

FIG. 1 illustrates an exemplary telemetry system 100 according to thedisclosure. Telemetry system 100 may include a local unit 105 and aremote unit 110, which are in communication with each other. Telemetrysystem 100 may be used in various industries and applications in whichmeasurements are collected at remote unit 110 and transmitted by remoteunit 110 to local unit 105 for processing, storage, and/or utilization.Applications may include, but are not limited to, flight testing,oil-pipeline monitoring, ocean monitoring, and the provision of healthcare. In these examples, remote unit 110 may be associated with objectsand/or environments of interests (e.g., a plane in the sky, a pipelineburied underground, a region of the ocean floor, and a patient in his orher private home) while local unit 105 may be associated with a locationand/or user that is not immediately near remote unit 110. In anexemplary embodiment, remote unit 110 may be an airborne system andlocal unit 105 may be a ground system.

Local unit 105 may include a transmitter 106, a receiver 107, and/or atransceiver 108 for communicating with remote unit 110. Transmitter 106,receiver 107, and/or transceiver 108 may be configured for transmittingand/or receiving wireless and/or wired signals. Local unit 105 mayreceive a signal 115 via receiver 107 and/or transceiver 108 from remoteunit 110. Signal 115 may include data 116 such as measurements ofvarious parameters that are related to remote unit 110 or thesurroundings of remote unit 110. Data 116 may include other information,such as data structure information, which may be needed for properprocessing of data 116 by local unit 105, as explained in further detailbelow. Local unit 105 may include a processor 120, a memory 121, and/ora secondary storage 122, and any other components for handling data 116.Processor 120 may include one or more known processing devices such as acentral processing unit (CPU), digital signal processing (DSP)processor, etc. Processor 120 may execute various computer programs toperform various methods of processing signal 115 and/or data 116. Memory121 may include one or more storage devices configured to storeinformation related to the execution of computer programs by processor120. Secondary storage 122 may store computer programs and/or otherinformation, including data 116 or information derived from data 116.Storage 122 may include volatile, non-volatile, magnetic, semiconductor,tape, optical, removable, non-removable, non-transitory, transitory,and/or other types of storage device or computer-readable medium.

Local unit 105 may also transmit a signal 117 via transmitter 106 and/ortransceiver 108 to remote unit 110. In an exemplary embodiment, localunit 105 may be configured to automatically transmit signal 117 toremote unit 110 based on data 116. For example, local unit 105 maytransmit feedback commands via signal 117 to remote unit 110 based onreceived data 116. In another exemplary embodiment, local unit 105 maybe configured to receive commands manually-inputted by a user that arethen transmitted to remote unit 110 via signal 117. In yet anotherexemplary embodiment, signal 117 may include software updates and/or anyother data that may be useful for remote unit 110.

Remote unit 110 may include a transmitter 111, a receiver 112, and/or atransceiver 113 for communicating with local unit 105. Transmitter 111,receiver 112, and/or transceiver 113 may be configured for transmittingand/or receiving wireless and/or wired signals. Remote unit 110 mayinclude one or more transducers 125, which generate measurement data126. Transducers 125 may be any device which converts signals in oneform of energy to another form of energy. For example, transducers 125may include various sensors, for example, a temperature sensor (such asa thermocouple that converts a heat signal into an electrical signal), amotion sensor (such as an accelerometer that converts acceleration intoan electrical signal or an inertial measurement unit (IMU) that measuresvelocity, orientation, acceleration, and/or gravitational forces using acombination of accelerometers and gyroscopes), and/or an electromagneticwave receiver (such as an antenna which converts propagatingelectromagnetic waves into an electrical signal, e.g. GPS antenna).Other examples of transducers 125 include a pH probe, a hydrogen sensor,a piezoelectric crystal, a pressure sensor, a hydrophone, a sonartransponder, and/or a photodiode. Such list is not meant to beexhaustive. The above examples include converting a non-electricalsignal to an electrical signal, but this disclosure is not limited tosuch examples. For example, transducer 125 may be a galvanometer, whichconverts electrical signals into a mechanical motion such as rotarydeflection, and/or a speaker which converts electrical signals into airpressure.

The resulting signals generated and output by transducers 125 arereferred to as measurement data 126 for purposes of this disclosure.Measurement data 126 may be related to the health and status of variouscomponents of remote unit 110, e.g., motors, electronics, payloads,and/or any other component of remote unit 110. Measurement data 126 mayalso be related to the operational status and/or kinematic state ofremote unit 110 as a whole. Measurement data may be further related tothe location and/or environment of remote unit 110.

Remote unit 110 may process the data 126 generated by transducers 125and transmit them, via transmitter 111 and/or transceiver 113, as partof data 116 in signal 115 to local unit 105. Remote unit 110 may includea processor 130, a memory 131, and/or a secondary storage 132, asdescribed above for local unit 105, for processing and/or storing thedata from the signals generated by transducers 125 and other data.Remote unit 110 may receive signal 117, via receiver 112 and/ortransceiver 113, from local unit 105. Signal 117 may include variousdata as discussed above in the description of local unit 105.

Various components may be implemented by processors, e.g., processor 120and/or processor 130, wherein the illustrated components are implementedas software code that is executed by processor 120 and/or processor 130to perform functions described below. Thus, while additional featuresand functions of local unit 105 and remote unit 110 are described above,these functions may be performed by processor 120 and/or processor 130.

With reference to FIG. 2, remote unit 110 may also include a dataclassifier 210, a compression encoder 215, a bit stream shuffler 220, anencrypter 225, and a packetizer 230, which process and prepare data 126for transmission to local unit 105. This processing may includecompressing data 126. By classifying data 126 via data classifier 210,as discussed in further detail below, data 126 may be able to becompressed by compression encoders 215 with relatively high compressionratios and relatively low delay and computational costs. Thecompression-encoded data may be output as bit-streams, and bit-streamshuffler 220 may shuffle these bit-streams to remove possiblypredictable patterns added by compression encoders 215. Encrypter 225may encrypt the shuffled bit-stream, and packetizer 230 may performpacket protocol packetization for distributing the encrypted bit-streamas packets according to predefined protocols, such as TCP or UDP/IP. Thepackets may subsequently be transmitted to local unit 105 as data 116(see FIG. 1). Local unit 105 may process the received packets with asimilar process but in reverse order to produce reconstructed data 126that may be identical to the data generated at remote unit 110. Localunit 105 may include a depacketizer 235, a decrypter 240, a bit-streamdeshuffler 245, a compression decoder 250, and a data mixer 255, all ofwhich process and reconstruct data 126. In an exemplary embodiment,disclosed systems and methods may be able to reach a 5:1 compressionratio for various data 126.

Data classifier 210 of remote unit 110 may receive, as input, data 126,which may include measurement data generated by transducers 125 andother data such as data structure information, and classify data 126into different categories. In an exemplary embodiment, data classifier210 may classify measurement data 126 into three categories: (1)pulse-code modulation (PCM) encoded measurement data 260, (2) image data261, and (3) other data 262. In another exemplary embodiment, dataclassifier 210 may classify measurement data 126 more specifically into:(1) PCM-encoded measurement data, (2) image data, and (3) data structureinformation.

Remote unit 110 may convert measurement data 126 from transducers 125into PCM-encoded measurement data 260. PCM is a well-known encodingscheme and, as such, is not further explained in detail. Linear PCM, inwhich signal levels are quantized and indexed based on constantquantization intervals, will be referred to as LPCM. Logarithmic PCM, inwhich signal levels are segmented into unequal quantization segments,and each quantization segment in divided and indexed based on equalquantization intervals within the segment, will be referred to aslogarithmic PCM. The term PCM will generally refer to either or bothLPCM and logarithmic PCM. Such PCM-encoded data 260 may be a componentof data 126. Data 126 may also include image data 261. Image data 261may be data streams from, for example, at least one IR- and/orvisible-light video cameras, which may correspond to one of transducers125. Image data 261 may have frame rates of around 10 to 50 Hz whilePCM-encoded data 260 may have sampling rates of around 100 Hz. Imagedata 261 may comprise still images (e.g., photographs) and/or video,which are variable in two dimensions, as compared to data encoded asPCM-encoded data 260, which usually varies in one dimension, e.g.,amplitude. In an exemplary embodiment, PCM-encoded measurement data 260may make up 40% and image data 261 may make up 50% of data 126. Theremaining portion of data 126 may be other data 262.

After data classifier 210 classifies data 126 into different categories,each category of data may be compressed by a different compressionencoder 215. PCM compression encoder 215A may be configured forcompressing PCM-encoded measurement data 260 with low-delaylow-complexity lossless compression. Because PCM sequences may bederived from generally continuous analog signals, PCM-encodedmeasurement data 260 may exhibit a continuity in its data sequences.Stated differently, sequential data values may be correlated, whichallows for an exemplary PCM encoder 215A to use prediction models forthe purpose of reducing redundancy in the data and facilitating highercompression ratio. Redundancy may be based on the number of bits used tocode a message minus the number of bits of actual information in themessage. For an exemplary PCM-encoded measurement data 260 generatedwith a sampling rate of 100 Hz, a 10 millisecond delay allows theprediction model to look at one sample ahead in time. PCM compressionencoder 215A may determine a prediction model of the data by analyzingthe correlation among input PCM-encoded measurement data 260 that hasbeen obtained. An exemplary model may be a linear prediction model suchas:y _(i)=Σ_(k=1) ^(n) a _(k) x _(i−k) +b ₁ x _(i+1)  (1)where x_(i) is the i^(th) sample in the PCM-encoded measurement data 260if LPCM, or is the quantization index of the i^(th) sample in thePCM-encoded measurement data 260 if logarithmic PCM. y_(i) is thecorresponding predicted value of the sample. n is the order of theprediction model. a_(x) and b₁ are prediction coefficients, which PCMcompression encoder 215A may determine by the analysis of data sequencesof input PCM data that has been obtained. The prediction coefficientsmay be calculated offline using optimization algorithms, trained using aset of known data, and/or real-time adapted. The coefficients maytherefore be adjusted based on different specific data sequences.

The predicted value y_(i) generated by the prediction model and theactual value x_(i) of the sample may be almost the same, and therefore adifference signal z between x_(i) and y_(i) may be made up of mostlyzeroes. As a result, PCM compression encoder 215A may be able tocompress the difference signal z with a high compression ratio. In anexemplary embodiment, PCM compression encoder 215A may compress thedifference signal z using an entropy encoding scheme such as Huffmancoding or arithmetic coding. PCM compression encoder 215A may use otherknown encoding schemes including, for example, run-length encoding.

FIG. 3 illustrates a detailed block diagram of low-delay low-complexitylossless PCM compression encoder 215A₁ as an exemplary embodiment of PCMcompression encoder 215A of FIG. 2, in which the input PCM-encodedmeasurement data 260 is LPCM data 310. LPCM data 310 may be input into aprediction model selector/adapter 315 which determines the predictionmodel 320 to be utilized based on LPCM data 310, as described above. Theprediction model 320 that is determined by prediction modelselector/adapter 315 may receive LPCM data 310 and generate a prediction325 of the LPCM data according to, for example, Equation 1. Differenceunit 330 may receive LPCM data 310 and prediction 325 of the LPCM dataand calculate a difference signal 335 between the two. Encoder 340 maythen compression encode difference signal 335 before it is packetizedfor transmission.

FIG. 4 illustrates a detailed block diagram of a low-delaylow-complexity lossless PCM compression encoder 215A₂ as an exemplaryembodiment of PCM compression encoder 215A of FIG. 2, in which the inputPCM-encoded measurement data 260 is logarithmic PCM data 410. Asdescribed above, logarithmic PCM data 410 may include valuescorresponding to quantization segments and quantization indexesassociated with the various segments. Often for logarithmic PCM data410, the variation of segment values may be slow, therefore PCMcompression encoder 215A₂ may be able to encode the data moreefficiently by separately encoding the quantization segments andquantization indexes. PCM compression encoder 215A₂ may include a PCMswitch 415, which may determine whether to encode logarithmic PCM data410 with separate encodings for the quantization segments andquantization indexes, or convert PCM data 410 to LPCM data 310. Thisdetermination may be made based on the level of variation of thequantization segments of logarithmic PCM data 410. If the variation ofquantization segments is slow, PCM switch 415 may determine that thedata should remain as logarithmic PCM data 410. Segment encoder 420 mayreceive a segment data stream 422 comprising the bits of logarithmic PCMdata 410 that correspond to the quantization segments, and predictionmodel 425 may receive an index data stream 427 comprising the bits thatcorrespond to sign and quantization indexes. Segment encoder 420 mayencode segment data stream 422 with, for example, run-length encoding.Index data stream 427 may be encoded using predictive encoding usingprediction models, similarly as described above with respect to FIG. 3.That is, prediction model 427, which may be selected by prediction modelselector/adapter 430 based on characteristics of logarithmic PCM data410, may receive index data stream 427 and generate a prediction 432 ofthe index data stream. Difference unit 435 may determine a differencebetween prediction 437 and index data stream 427, which may then beencoded by encoder 440. If the variation of the quantization segments isfast, PCM switch 415 may determine that the data should be converted toLPCM data 310, by PCM-to-LPCM converter 450. After conversion, LPCM data310 may be encoded, as discussed above with respect to FIG. 3. Theencoded PCM-encoded data may then be packed into a bit stream fortransmission.

With reference back to FIG. 2, remote unit 110 may include imagecompression encoder 215B for encoding image data 261. In an exemplaryembodiment, image data 261 may be video data. As such, image compressionencoder 215B may compress image data 261 based on correlation inside avideo frame and among adjacent frames. FIG. 5 illustrates a blockdiagram for a video compression scheme 215B₁ as an exemplary embodimentof image compression encoder 215B of FIG. 2. Generally, a predictionmodel for video may be used to remove intra-frame or inter-frameredundancy. The residual data, which is the difference between theactual data and the predicted data, may be mostly close to zero. Theresidual may then be encoded using an entropy encoding scheme, such asHuffman coding, run-length encoding, arithmetic coding, dictionarycoding, and/or other known entropy coding. Image compression encoder215B may utilize a variety of lossless video codecs, e.g., MovingJPEG-LS, Moving JPEG 2000, Lagarith, Huffyuv, Alparysoft, x.264lossless, CorePNG, FV1, MSU, MPEG-2, MPEG-4, H.262, H.263, H.264 and/orothers. Moving JPEG-LS may provide a better compression ratio at fourtimes the rate of Moving JPEG 2000. The Lagarith lossless video code maybalance between compression ratio and computation complexity.H.264/MPEG-4 Part 10 or AVC (Advance Video Coding) may provide the bestperformance in terms of compression ratio and compressed video quality.H.264/MPEG-4 AVC is currently the most widely adapted compressionstandard for high definition video. H.264/MPEG-4 AVC has a variety ofimplementation and optimizations available on different processors.Achievable compression ratios may highly depend on the content of imagedata 261. In an exemplary embodiment, the compression ratio that theabove-listed video codecs may reach may be on average about 2:1 to 3:1.In some situations, a higher compression ratio of, for example, 5:1, maybe desirable.

To obtain higher compression ratios with low delay and low complexity,the image compression encoder may utilize knowledge of some data sourcecharacteristics, which may be determined using methods ofcontent-adaptation and determination of specific parameter settings.FIG. 6 illustrates a block diagram for an image compression encoder215B₂ as an exemplary embodiment of image compression encoder 215B ofFIG. 2 for compressing video-type image data 261, based on knowledge ofthe data content. Image compression encoder 215B₂ may include apreprocessor 610 for normalizing image data 261 into normalized videosequences 611 and extracting offset information 612. The offsetinformation 612 may be compressed using a time-domain linear predictionmodel followed by entropy encoding, similar to the process described forFIG. 3, in that an offset prediction model 615 generates a prediction617 of offset information, a difference unit 618 determines thedifference 616 between actual offset information 612 and prediction 617,and encoder 619 encodes the difference 616 using entropy encodingschemes. Offset information 612 often is constant or slow-varying for agiven imagery sequence in image data 261, in which case encoder 619 mayutilize, for example, run-length encoding. An intra-frame predictionmodel 620 and an inter-frame prediction model 621 may receive normalizedvideo sequences 611. Intra-frame prediction model 620 may handlecompression encoding related to data within one frame of normalizedvideo sequences 611, and inter-frame prediction model 621 may handlecompression encoding related to data of several frames of normalizedvideo sequences 611. For inter-frame prediction model 621 to be able tocompress image data 261 based on more than one frame, video frame buffer625 may buffer one or more of the frames.

For example, the rate of video sequences 611 may vary from 10 Hz to 50Hz. If maximum delay requirement is 10 milliseconds, inter-frameprediction model 621 may be able to look ahead up to two frames. Eachframe of normalized video sequences 611 may be categorized into one ofthree different types of frames by image compression encoder 215B₂before being encoded using intra-frame prediction model 620 orinter-frame prediction model 621. The three categories of frames are:I-frames, P-frames, and B-frames. An I-frame (intra-frame) is aself-contained frame that can be independently decoded without anyreference to other frames. A P-frame (predictive inter-frame) is a framethat makes references to parts of an earlier I- and/or P-frame to codethe frame. A B-frame (bi-predictive inter-frame) is a frame that makesreferences to both an earlier reference frame and a future referenceframe. In general, I-frames require more bits than P- and B-frames, butthey are robust to transmission errors. P- and B-frames are sensitive totransmission errors, and B-frames introduce delays. Image compressionencoder 215B₂ may determine which type a particular frame is based ondetermination of the prediction errors, i.e. the difference between theprediction of either model and the actual data. If image compressionencoder 215B₂ determines that a frame is an I-frame, difference unit 630determines a difference 631 (i.e., the residual) between the prediction624 generated by intra-frame prediction model 620 and the actual framedata, and encoder 632 encodes the difference signal. In an exemplaryembodiment, encoder 632 may encode the difference signal using entropyencoding such as arithmetic coding. Otherwise, image compression encoder215B₂ may determine that the frame is a P- or B-frame, and inter-frameprediction model 621 may generate a prediction signal 626 based on thecorrelation between multiple frames. This temporal correlation resultsin compressible P- and/or B-frame bit streams, which may be compressedby encoder 635.

Image compression encoder 215B₂ may incorporate knowledge of thecontents of image data 261 to achieve higher compression ratios. In someembodiments, image compression encoder 215B₂ may analyze a video frameor a sequence of video frames and divide its contents into differentshapes or object regions. Image compression encoder 215B₂ may thencompress each object independently to remove higher order correction inthe frame or among the sequence of frames. The extracted objects may berepresented efficiently by combining them with known characteristics ofobjects in the frames (spatial characteristics and/or temporalcharacteristics such as the motion of an object). Such content-basedvideo coding may improve coding efficiency, robustness in error-proneenvironments, and content-based random access. Some content-based codingtechniques suffer from very expensive computation costs due tosegmentation, motion estimation, and overhead of object presentation(e.g., shape presentation or object descriptions). However, by utilizingprediction models that produce residuals that are then entropy coded,accurate object/shape segmentation and motion estimation may be lesscrucial. Accordingly, in an exemplary embodiment, image compressionencoder 215B₂ may avoid object presentations by using content blockswith flexible block sizes and computer complexity for coarsesegmentation and motion estimation.

FIG. 7 illustrates a block diagram of an intra-frame prediction model620 ₁ as an exemplary embodiment of intra-frame prediction model 620 ofFIG. 6 for generating predicted blocks of video frame 705. Segmenter 710may receive and apply segmentation to frame 705. The segmentation maydivide frame 705 into non-overlapped regions, wherein each region isconsidered to be an object. Partitioner 715 may then partition theentire frame 705 into overlapped object blocks with flexible sizes andlocations. Each object block will be associated with either aprediction, by a linear prediction model 720, or a defined object asstored in an object database 725. Object database may include twocategories of objects: pre-defined static objects and real-time adaptedobjects. An object-searching algorithm 730 may first search thereal-time objects and then the predefined objects. An update algorithm735 may update the real-time objects. The output of the intra-frameprediction model 620 ₁ may comprise predicted block which best matchesobjects from the database or the predicted block output by linearprediction model 720, based on whichever minimizes total predictionerrors. As is known in the art, a segment may be an object with specificdelineations and bounds, and a partition may be a portion of the imagedata that includes multiple objects that is used to calculatecompression across two segments.

FIG. 8 illustrates a block diagram for an inter-frame prediction model621 ₁ as an exemplary embodiment of inter-frame prediction model 621 ofFIG. 6. Temporal correlation estimator 810 may receive partitionedobject blocks of one frame, in a sequence of video frames 805, andcompare them with object blocks of previous frames. For example,comparisons may be made based on numbers, shapes, and/or intensities ofthe object blocks. Temporal correlation estimator 810 may decide theframe type based on the similarity of the object blocks between frames.If the level of similarity is low, the current frame may be encoded asI-frame using intra-frame prediction model 620. Otherwise, if the levelof similarity is high enough, the frame may be encoded using the othercomponents of inter-frame prediction model 621 ₁. Motion detector 815may receive the sequence of video frames 805 from video frame buffer 807that includes the frame that is to be encoded. Motion detector 815 mayidentify motion objects from background objects. For background objectblocks, a temporal prediction model 817 or interpolation may be applied,and a residual calculator 819 may calculate the prediction residual. Theprediction residual may then be encoded with entropy encoding. Formoving objects, motion compensation may be generated by a motion model820 and motion estimator 825. That is, motion model 820 and motionestimator 825 may generate a prediction 827. If the prediction residualis large, then the current frame may be redefined as I-frame. Otherwise,motion compensation vectors which predict the motion of object andprediction residuals will be encoded. Motion detection and compensationare key components in advanced video codec. Usually, they consume thelargest amount of computation time. For example, motion estimation usesabout 60-80% of H.264 encoding time. To reduce the complexity of motionestimation, inter-frame prediction model 621 ₁ may utilize motion models820 which are defined by known knowledge of a given video source. Motionmodel 820 may list all possible motions, such as type, direction, speedlimit, etc. for a given video source. Motion estimator 825 may utilize asimple motion estimation algorithm, such as small region diamond search,followed by adaptive motion vector refinement. The output of inter-frameprediction will provide encoding frame type and corresponding encodinginformation. Afterwards, this output may be further compressed usingentropy encoding via encoder 635, as described for FIG. 6, for example.

In another embodiment, widely used image and video compression standardscan be used with image data 261. For example, image compression encoder215B may utilize H.264 AVC, which may be a suitable video codec forrobust and reliable video compression for various embodiments of thisdisclosure. H.265 HEVC may be able to provide the same video quality asH.264 AVC with twice the compression ratio and complexity. Differentreference implementations of H.265 HEVC are currently available fordemonstration purposes. This codec may be used in various exemplaryimplementations of disclosed methods. Additionally, another compressionstandard is in CCSDS (Consultative Committee for Space Data Systems)lossless data compression, which was designed for satelliteapplications. The CCSDS lossless compression standard may be implementedwith or instead of previous video codecs to achieve visually losslessvideo compression algorithms with low complexity.

With reference back to FIG. 2, remote unit 110 may include othercompression encoder 215C for encoding other data 262 that is notPCM-encoded measurement data 260 or image data 261. Other data 262 mayinclude already compressed data. In an exemplary embodiment, encoder215C may group data sources that are highly correlated together andseparate out data that is already compressed. Often, foralready-compressed data, it may be difficult to obtain further data sizereduction. Depending on the coding efficiency of the originalcompression algorithms, other compression encoder 215C may determinewhether to keep the same compressed data or to decompress the compresseddata and recompress it using any of the various encoding schemesdescribed above, for example, using PCM compression encoder 215A forLPCM or logarithmic PCM. For data whose contents are numerical values,such as floating point values instead of linear PCM, other compressionencoder 215C may determine that LPCM algorithms described above can beused to compress them. If the data's content is symbols, such as textmessages, other compression encoder 215C may analyze the datacharacteristics and choose an appropriate entropy encoding scheme. Ingeneral, other compression encoder 215C may attempt to group correlateddata together as multi-dimension vectors, establish correlation modelamong group members to reduce redundancy, and then apply a losslesscompression algorithm.

In some exemplary embodiments, other data 262 may be specifically datastructure information. Because data 126 may include data from varioussources (e.g., measurement data 126 from multiple transducers 125, someof the measurement data being specifically image data 261), exemplarydata 126 may be structured such that for one predetermined period oftime (a frame), the various data signals are time-multiplexed andserialized according to a predefined structure. Data structureinformation may be provided as part of data 126 so that local unit 105is able to synchronize to the periodic data and identify or extractspecific types of data. Data structure information may include framesynchronization words (SYNC1 AND SYNC 2), sub-frame identification words(SFID), sub-sub frame synchronization words (XCounter), minor framecounters (MCounter), other counters (YCounter), and other known datastructure information. Various exemplary embodiments for low-delay,low-complexity compression algorithms for SFID, SYNC1 and SYNC2 arediscussed in further detail.

The SFID component may vary slowly, increasing or decreasing as theframe increases. As SFID varies with different numbers of frames, thekey to a compression algorithm for SFID is to identify the variationdirection and the number of frames for each SFID. The periodicity of theSFID variation may be extracted as a feature and used to design alow-delay, low-complexity, error-propagation-robust, adaptive-predictionalgorithm to compress the SFID component. The format of the encoded bitstream may be defined as shown in Table 1. Test results show that thecompression ratio varies with data memory size and packet size. If thepacket size of data being transmitted from remote unit 110 to local unit15 is one major frame per packet, the compression ratio is about 613:1.Although other formats may be used for encoding the bit-stream based onthe characteristics of the particular SFID data, as well as otherconsiderations, an exemplary format for encoding the bit stream as shownin Table 1 may be used.

TABLE 1 SFID Encoding Stream # of 16-bit Words Contents Values in LogFile 1 Initial SFID value 49067 2 Up/down Indication 1: Increase/0:decrease 3 # of peaks of SFID variation (N) 1 4 (3 + 1) Peak value 150431 . . . . . . 3 + N − 1 Peak value N − 1 3 + N Peak value N 3 + N +1 # of varying periods (M) 215 4 + N + 1 Period 1 3737 4 + N + 2 # ofrepeats of Period 1 1 4 + N + 3 Period 2 6250 4 + N + 4 # of repeats ofPeriod 2 27 . . . . . . 4 + N + 2M − 1 # of repeats of Period M 1437 4 +N + 2M Period M 1

The two 16-bit synchronization words (SYNC1 and SYNC2) typicallyrepresent constants. However, they may be changed during systemoperation. A low-delay, low-complexity lossless compression algorithmfor the synchronization may be based on monitoring the synchronizationwords. Table 2 shows the structure of their encoding bit stream. Thealgorithm may exceed a 400:1 compression ratio when one major frame isused per packet. Although other formats may be used for encoding the bitstream based on the characteristics of the particular synchronizationdata, as well as other considerations, an exemplary format for encodingthe bit stream as shown in Table 2 may be used.

TABLE 2 The Encoding Bit Stream of SYNC Words # of 16-bits wordsContents Values in Log File 1 Sync Word 1 49067 2 Sync word2 1:Increase/0: decrease 3 Process_size In # of minor frames 4 Change flagfor block 1 Change: 1 or # of changes (N) No change: 0 5 # of changesfor both sync1 & 2 N12 5 + 3(1~3) (1^(st) pos, l^(st) SW1 and SW2) . . .5 + 3(N12 − 2~N12) (N12th pos, N12th SW1 & SW2) 215 5 + 3N12 + 1 # ofchanges for sync1 6 + 3N12 + (1~2) (1^(st) pos, 1^(st) SW1) 1 . . . 62506 + 3N12 + 2(N1 − 1~N1) (N1^(th) pos, N1^(th) SW1) 1 6 + 3N12 + 2N1 + 1# of changes for sync2 7 + 3N12 + 2N1 + (1~2) (1^(st) pos, 1^(st) SW2) 1. . . 6250 7 + 3N12 + 2N1 + 2(N2 − 1~N2) (N2^(th) pos, N2^(th) SW2) 1Change flag for block 2 4 + N + 2M − 1 # of repeats of Period M 1437 4 +N + 2M Period M 1

For data structure information and PCM-encoded measurement data 260, inaddition or as an alternative to the above-disclosed methods ofcompression, e.g., as described above with respect to PCM compressionencoder 215A and other compression encoder 215C, a multistate losslesscompression algorithm may be utilized. FIG. 9A illustrates a statemachine of an exemplary multistate lossless compression including one offive possible states that PCM-encoded measurement data 260 and/or datastructure information (i.e., other data 262) is in: constant level,slow-varying, periodical, transition, and random noise. FIG. 9Billustrates a block diagram of an exemplary multi-state, low-delay,low-complexity lossless compressor 900, including multi-state encoder910 and multi-state decoder 940. Multi-state encoder 910 may be part ofremote unit 110 and multi-state decoder 940 may be part of local unit105. Multi-state encoder 910 may include a group builder 915, a tilingoperator 920, a state detector 925, and various compression encoders 930(e.g., constant value compressor 931, adaptive waveform encoder 932, andnoise encoder 933). PCM-encoded measurements and/or data structureinformation may be input into group builder 915 to be grouped intosimilar measurements. Tiling operator 920 may divide each group intonon-overlapping time segments, and state detector 925 may determine thestate of each of the segments. According to the determined state, thesegment may be compression encoded by one of encoders 930 before beingpacked into a bit-stream that may be transmitted to local unit 105 aftershuffling, encryption, and packetization (see FIG. 2).

FIG. 9B also illustrates a block diagram of an exemplary multi-statedecoder 940, which may reconstruct the data encoded by multi-stateencoder 910 after it is received by local unit 105 and after it isdepacketized, decrypted, and deshuffled (see FIG. 2). Multi-statedecoder 940 may reconstruct the data by performing the reverse of thesteps performed by multi-state encoder 910. Using various compressiondecoders 945 (e.g., constant value generator 946, adaptive waveformdecoder 947, and noise decoder 948) and a post-processing unit and mixer950. Additional details of multi-state low-delay, low-complexitylossless compressor 900 are described with respect to FIGS. 10-17.

FIG. 10 illustrates a flowchart for an exemplary method of multi-statelossless compression as performed by remote unit 110, or morespecifically multi-state encoder 910, for example. At Step 1010,multi-state encoder 910 (more specifically, group builder 915) may groupsimilar PCM-encoded measurement data 260 together and/or data structureinformation 262. Although all of PCM-encoded measurement data 260 may becorrelated to each other to different degrees, in an exemplaryembodiment and for simplicity, PCM-encoded measurement data 260 may bedivided into the following groups: VIBE, GPSR, R3, S1, GPS, IMU, andAnalog. The grouping may be made according to the definition of thetelemetry file or the hardware interface. VIBE may correspond to variousvibrometer measurements, which most of the time are stable and varyperiodically, although sometimes the measurements have suddenfluctuations for certain periods of time. The various vibrometermeasurements may be highly correlated. GPSR may correspond to Javad GPSdata that includes timing and global position data. R3 may correspond toroll-stabilized inertial measurement unit data for spinning vehicleapplications. S1 may correspond to IMU data that measures and reportsvelocity, orientation, acceleration, and gravitational forces related toremote unit 110. GPS may correspond to Global Positioning System datathat includes date, time, Cartesian position, Cartesian velocity,latitude, longitude, altitude, geodetic position.

At Step 1015, multi-state encoder 910 may divide each group equally intonon-overlapped segments to minimize delay. The step of dividing eachgroup of measurements into non-overlapping equal-length segments may bereferred to as a tiling operation and may be performed by tilingoperator 920. The size of the segments may be directly related to thelevel of acceptable delay and the data memory requirements. In anexemplary embodiment, multi-state encoder 910 may select a multiple ofmajor frame duration as the sizes of the segments. Measurements ofdifferent groups may use different segment sizes. In another exemplaryembodiment, multi-state encoder 910 may select a segment size that isthe same as the transmission packet duration to ensure that the delaydue to compression does not affect the transmission during real-timeoperation of telemetry system 100.

For each segment, multi-state encoder 910 may identify the state towhich the segment belongs and apply different compression modelsaccording to the states. At Step 1020, multi-state encoder 910 mayclassify a current segment X into one of the following states: constant,periodical, slow-varying, transition (rapid jump or sudden change), orrandom noise, and then compress the segment accordingly. In an exemplaryembodiment, the default classification may be the transition state untilmulti-state encoder 910 has identified segment X as satisfying thecondition for a different classification.

FIG. 11 illustrates an exemplary method of classification of a currentsegment X in greater detail. At Step 1110, for a given segment X,multi-state encoder 910 may calculate the 1^(st) order derivative of thesignals of segment X to determine if segment X is constant. At Step1115, multi-state encoder 910 may check if at least 90% of the firstorder derivatives have a value of zero. If at least 90% are zero (Step1110: YES), multi-state encoder 910 may classify segment X as being in aconstant state, and therefore segment X may be compressed by constantvalue compressor at Step 1120. Otherwise (Step 1110: NO), multi-stateencoder 910 may determine if segment X exhibits periodicity bycalculating cross-correlation coefficients for segment X according toEq. 2, at Step 1123.

$\begin{matrix}{{R(\tau)} = \frac{E\left\lbrack {\left( {X_{t} - \mu} \right)\left( {X_{t + \tau} - \mu} \right)} \right\rbrack}{\sigma^{2}}} & (2)\end{matrix}$Equation 2 is more specifically an autocorrelation R(τ) of segment X asa function of time-shifts (τ), i.e., a correlation of segment X withitself. If the 1^(st) peak of R(τ) (not at the origin) is greater than athreshold (Step 1125: YES), multi-state encoder 910 may classify thatsegment X as periodic, and segment X may be encoded and compressed by anadaptive waveform encoder, at Step 1130, for encoding as a periodicsignal. Otherwise (Step 1125: NO), multi-state encoder 910 maydetermine, at Step 1135, various signal statistic features of segment Xincluding, for example, short- and long-term means, minimum, maximum,and variance of amplitudes in segment X, and use the signal statisticfeatures to determine whether segment X is steady. In an exemplaryembodiment, multi-state encoder 910 may determine whether segment X issteady (at Step 1140) if segment X satisfies either of the followingconditions, where Ds represents the derivative of the signal. Condition1 is:Ds _(max) /Ds _(min)<4  (3)andDs _(max) /D1<3  (4)where Ds_(max)=max{D_(S)}, D_(Smin)=min{Ds},Ds(i)=A_(Smax)(i)−A_(Smin)(i), and D1=A_(max)−A_(min), and whereA_(Smax) is the maximum amplitude of segment X, A_(Smin), is the minimumamplitude of segment X, A_(Smax)(i) is the maximum amplitude of thei^(th) term period in segment X, and A_(Smin)(i) is the minimumamplitude of the i^(th) short term period in segment X. Condition 2 is:(Std_(max)−Std_(min))/Std_(min)<1  (5)where Std_(max)=max {std(i)}, Std_(min)=min{std(i)}, and Std(i) is thestandard deviation of the i^(th) short term period.

If segment X satisfies at least one of the above conditions (1) or (2)(Step 1140: YES), multi-state encoder 910 may determine that segment Xis in a steady state and proceed to Step 1150. If segment X satisfiesneither of the above conditions (1) or (2) (Step 1140: NO), multi-stateencoder 910 may classify segment X as in a transition state, and segmentX may be compressed by a transition signal compressor at Step 1145.Otherwise, if multi-state encoder 910 determines that segment X is in asteady state, multi-state encoder 910 may further determine whethersegment X is a slow-varying signal or a random noise segment bydetermining the zero-crossing rate (ZCR) of the signals in segment X, atStep 1150, and comparing whether the ZCR is greater than the predefinedthreshold, at Step 1155. The zero-crossing rate is the rate at whichsignals in segment X cross the zero-value from a positive value to anegative value and vice versa. If the ZCR is greater than the threshold(Step 1155: YES), multi-state encoder 910 may determine that segment Xis a noise segment and segment X may be compressed by a random noisecompressor at Step 1160. If the ZCR is lesser than the threshold (Step1155: NO), multi-state encoder 910 may classify segment X as being in aslow-varying state and segment X may be compressed by a slow variationsignal compressor at Step 1165.

A description of the various exemplary compressors for different statesis given below. An exemplary embodiment of constant value compressor 931may utilize run-length encoding because the majority of the data insegment X that is in the constant level state will be a single constantvalue. In another exemplary embodiment, constant value compressor 931may be implemented using discontinuous transmission with extraredundancy for error correction. That is, some of the constant valuesignal may be represented by the absence of signal. In an exemplaryembodiment, the non-constant values may be encoded as differentialcoding or linear prediction coding depending on their location and thepattern of occurrences. The complexity and memory requirement of theconstant value compressor 931 may be very low, and the compressor may beable to reach a compression ratio on the order of hundreds to onewithout any information loss. The constant value compressor 931 may beused to compress, for example, the major frame counters (SYNC1, SYNC2),some segments of GPS data, and temperature monitoring data.

Adaptive waveform encoder 932 may be used to compress periodical,slow-varying, and transition state signals, i.e., adaptive waveformencoder 932 may be used as a periodic-waveform compressor used at Step1130, a transition signal compressor used at Step 1145, and/or a slowvariation signal compressor used at Step 1165. FIG. 12 illustrates anexemplary adaptive waveform encoder 932 ₁. Adaptive waveform encoder 932₁ may include a skeleton extractor 1215, a periodic-signal compressionunit 1220, a prediction model-based compression unit 1225, askeleton-based compression unit 1230, and a bit-stream generator 1235.In an exemplary embodiment, theme extractor 1215 may extract themeskeletons based on the state of segment X. If the segment is periodical,theme extractor 1215 may construct a skeleton based on the first periodof segment X. Otherwise, theme extractor 1215 may divide segment X intosub-segments with equal length, and construct a skeleton based on thefirst sub-segment. A skeleton may be defined by a sequence ofbinary-coded values of amplitude and a corresponding sequence ofamplitude-switching positions. Alternatively, a skeleton may be definedby a sequence of values, wherein each value is one of two possiblevalues, and a corresponding sequence of amplitude-switching positions.

FIG. 13 illustrates a flowchart for an exemplary method of extracting atheme skeleton from segment X as performed by skeleton extractor 1215 ofFIG. 12. At Step 1310, skeleton extractor 1215 may receive segment X,the determined state of segment X, and/or various extracted features ofsegment X. Skeleton extractor 1215 may check whether the state ofsegment X is periodical at Step 1315. If the state is periodical (Step1315: YES), skeleton extractor 1215 may set a skeleton length L to bethe period exhibited by segment X at Step 1320. If the state is notperiodical (Step 1315: NO), skeleton extractor 1215 may set the skeletonlength L to be 1/N of the length of segment X, where N is an integer, atStep 1325. For convenience, an L-length portion of segment X will bereferred to as segment X_(L), wherein X_(L) may be a period-lengthportion of segment X for periodic states or a 1/N sub-segment of segmentX for slowly-varying or transition states. At Step 1330, skeletonextractor 1215 may set a threshold to be equal to the middle valuebetween the minimum value and the maximum value in segment X_(L),specifically:Threshold=X _(min)+(X _(max) −X _(min))/2  (6)

At Step 1335, skeleton extractor 1215 may associate data points ofsegment X_(L) as S_(high) if the data point is greater than or equal tothe threshold determined by Eq. 6, or associate a data point of segmentS as S_(low) if the data point is lesser than the threshold. As aresult, a set of values may be associated with S_(high) and another setof values may be associated with S_(low). At Step 1340, skeletonextractor 1215 may remove outliers from S_(high) and S_(low). At Step1345, skeleton extractor 1215 may average S_(high) and average S_(low)to determine skeleton values. For example, skeleton extractor 1215 mayset the skeleton values to be either of these average values (S_(high)_(_) _(avg) and S_(low) _(_) _(avg)). At Step 1350, skeleton extractor1215 may determine switching positions for segment X_(L). For example,skeleton extractor 1215 may determine the switching positionscorresponding to the S_(high) _(_) _(avg) and S_(low) _(_) _(avg)skeleton values. Skeleton extractor 1215 may output the skeletoncomprising the set skeleton values and the switching positionscorresponding to these values, at Step 1355.

After extracting the skeleton from segment X, adaptive waveform encoder932, may proceed with processing segment X based on whether segment X isin a periodic, slow-varying, or transition state. FIG. 14 illustrates aflowchart for an exemplary method of processing segment X in a periodicstate. If segment X is periodic, periodic signal compression unit 1220of FIG. 12 may process segment X by dividing segment X intonon-overlapping periods, at Step 1410. At Step 1415, for each period,periodic signal compression unit 1220 may fine-tune the skeleton of theprevious period by slightly varying switching boundaries and skeletonamplitude values to minimize mean square errors between the skeleton andthe signal in the current period. At Step 1420, when the best skeletonis found and chosen, the residual signal may be calculated bysubtracting the skeleton from the signal in the current period. At Step1425, the skeleton differences and the residual signal may be encodedfor transmission.

If segment X is slow-varying, prediction model-based compression unit1225 may process segments X_(L) using one of four prediction models:2^(nd) order autoregressive (AR) prediction, 4^(th) order AR prediction,6^(th) order AR prediction, and linear extrapolation prediction.Prediction model-based compression unit 1225 may calculate AR predictioncoefficients by using a Levinson algorithm, and linear extrapolationprediction coefficients by using a Lagrange polynomial optimizationprocess, and corresponding residuals. The best prediction model willprovide the least number of bits requirement for representing the signalof segment X_(L). Prediction model-based compression unit 1225 maycompare the number of bits required with this best prediction model tothe number of bits required by the skeleton and residual model, in whichsegment X_(L) is represented by a skeleton model and a correspondingresidual. Prediction model-based compression unit 1225 may then choosethe best model between these two for segment X_(L), and encode thecorresponding parameters and residual data into a bit stream fortransmission.

FIG. 15 illustrates a flowchart for an exemplary method of processingsegment X in a slow-varying state. At Step 1510, skeleton extractor 1215and/or prediction model-based compression unit 1225 may extract theskeleton of the first (1/N) sub-segment X_(L) and calculate thecorresponding residual. At Step 1515, prediction model-based compressionunit 1225 may determine the total number of bits B_(SKELTON) needed toencode first sub-segment X_(L) using the skeleton-and-residual model. AtStep 1520, prediction model-based compression unit 1225 may compute therequired number of bits for 2^(nd) AR prediction, 4^(th) AR prediction,6^(th) AR prediction, and linear extrapolation prediction, and find theprediction model requiring the minimum number of bits B_(MINIMUM). AtStep 1525, prediction model-based compression unit 1225 may determinewhether B_(SKELETON) is lesser than B_(MINIMUM). If B_(SKELETON) islesser than B_(MINIMUM) (Step 1525: YES), prediction model-basedcompression unit 1225 may select the skeleton-and-residual model at Step1530. If B_(SKELETON) is not lesser than B_(MINIMUM) (Step 1525: NO),prediction model-based compression unit 1225 may select the AR or linearextrapolation prediction model corresponding to B_(MINIMUM) at Step1535. At Step 1540, prediction model-based compression unit 1225 mayencode segment X_(L) using the parameters and residual data of the modelselected at Step 1525.

If segment X is in a transition state, skeleton-based compression unit1230 may process segment X according to, for example, the exemplarymethod illustrated in the flowchart of FIG. 16. At Step 1610,skeleton-based compression unit 1230 and/or skeleton extractor 1615 mayextract the skeleton for each sub-segment X_(L) and calculate thecorresponding residual. At Step 1615, skeleton-based compression unit1230 may then determine the number of bits B_(SKEL-RESID) required forrepresenting each sub-segment X_(L) with the skeleton-and-residualmodel. At Step 1620, skeleton-based compression unit 1230 may determineskeleton differences for each sub-segment X_(L) based on a previoussub-segment X_(L). At Step 1625, skeleton-based compression unit 1230may determine the number of bits B_(DIFF) required to encode sub-segmentX_(L) based on only the difference between the current sub-segment X_(L)and the previous sub-segment X_(Lprev). At Step 1630, skeleton-basedcompression unit 1230 may determine whether B_(SKEL-RESID) is greaterthan B_(DIFF). If B_(SKEL-RESID) is greater than B_(DIFF) (Step 1630:YES), sub-segments X_(L) may be encoded using skeleton differences andresiduals at Step 1635. If B_(SKEL-RESID) is lesser than B_(DIFF) (Step1630: NO), sub-segments X_(L) may be encoded using full skeletonscorresponding to each sub-segment X_(L) and residuals at Step 1640.

For constant, periodic, slow-varying, and transition states, variouscompression methods as described above may result in desirablecompression ratios for segment X data signals. A small percentage of thetelemetry data, however, may show no or extremely low correlation incertain segments, and their waveform may look like random noise.Theoretically, such data may not be able to be compressed withoutinformation loss. There may be almost no compression for noise data,i.e. the compression ratio is about 1:1. FIG. 17 shows an exemplarycompression encoding method for noise data. At Step 1710, a noiseencoder may calculate the DC value of segment X, i.e., the mean value ofX, and a difference signal DIFF=X−mean(x). At Step 1715, the noiseencoder may pack the DC value and the DIFF signal into a bit-stream fortransmission.

It is contemplated that a compression encoding may be specially designedfor the compression of VIBE data. As all components of VIBE data may behighly correlated, it may be more efficient to compress them jointly.Based on the statistics of VIBE data, a compression algorithm maycategorize VIBE samples into 3 states: slow-varying, rapid-changing, andslow-varying with impulse. The algorithm may include a stable stage overa long-term period, which may be a minimum period of 50 frames and amaximum of 400 frames. Cross-correlation may be used to detect theperiod, and then a prediction model may be used based on the detectedperiod. In an exemplary embodiment, the compression algorithm mayinvolve automatic switches between states of slow variation and suddenfluctuation.

With reference back to FIG. 2, after data 126 has been compressed andencoded according to the characteristics of the data, bit-streamshuffler 220 may receive the compression-encoded data as a bit streamand proceed to shuffle the bit stream so as to remove possiblypredictable patterns added by the compression encodings. The potentiallypredictable patterns in compressed data are present when both theencoder and decoder share commonly defined bit allocations. Since anyparameters in the compressed domain are represented by a group ofcontinuous bits, the intrinsic connection among the parameters willappear as repeatable patterns in compressed bit streams when observedover a long enough period. To break the repeatable patterns, bit-streamshuffler 220 may be used to introduce multiple levels of randomness toproduce random bit allocation in the final bit-stream.

FIG. 18 shows details of a bit-stream shuffler 220, as an exemplaryembodiment of bit-stream shuffler 220 of FIG. 2. There may be fourlevels of randomness generated by bit-stream shuffler 220 ₁. The firstlevel of randomness may be generated by a global shuffler 1810, whichalternates big-endian and little-endian presentation every n bytes inthe bit stream. The second level of randomness may be generated by thecategory shuffler 1815, which randomly alternates category numbers(e.g., 1, 2, or 3) with predefined probabilities so that category typesare randomly located in the bit streams. The third level of randomnessmay be generated and introduced by a component shuffler 1820. In amanner similar to the category shuffler 1815, the component shuffler1820 may randomly allocate the order of data sources, so thatcorrelation among different data sources will not generate a repeatedpattern in the bit streams. The fourth level of randomness may beintroduced by a parameter shuffler 1825 such as parameter shufflers 1825₁, 1825 ₂, and/or 1825 ₃, which may randomly assign bit allocation inindividual encoding processes, based on segment states. For example,parameter shuffler 1825 ₁ may randomly assign bit allocation in theencoding process of segments in the transition state, parameter shuffler1825 ₂ may do the same for segments in the periodical state, andparameter shuffler 1825 ₃ may do the same for segments in theslow-varying state. Depending on the encryption algorithm, the shufflermay be configured to use different level shufflers. Different levelshufflers may be enabled or disabled independently by random levelcontroller 1830 to provide multiple combinations of randomness to removepotential threads for different security and complexity requirements.The common random level and random number generators may be sharedbetween the encoder in remote unit 110 and the decoder in local unit105. The various shufflers 1810, 1815, 1820, and 1825 do not affectcompression ratios. In local unit 105, bit-stream deshuffler 245 mayinclude specific deshuffler components that correspond to the variousshufflers 1810, 1815, 1820, and/or 1825. These deshufflers should usethe same seeds as their corresponding shufflers for the randomgenerators that generate the randomness applied by the bit-streamshuffler 220, and decoded by bit-stream deshuffler 245, so thatbit-stream deshuffler 245 may automatically reorder the encodedbit-streams to their original forms for decoding.

FIG. 19 illustrates bit-stream shuffler 220 ₂ as another exemplaryembodiment of bit-stream shuffler 220 of FIG. 2. A first level ofrandomness may be created based on data types, and more specificallybased on the number of data sources of each category. Date type shuffler1910 may randomly generate category numbers (e.g., 1, 2, and 3) withpredefined probabilities, so that the category types (e.g., PCM-encoded,image, and other) are randomly located in bit-streams. A second level ofrandomness may be introduced at the bit location of data sources. Datasource shufflers 1920 (including, e.g., 1920 ₁, 1920 ₂, and/or 1920 ₃)may randomly allocate the order of its data sources, so that thecorrelation among different data sources will not generate repeatablepatterns in the bit streams. For example, data source shuffler 1920 ₁may shuffle the order of different sources of PCM-encoded data, 1920 ₂may shuffle the order of different sources of video sequences data, and1920 ₃ may shuffle the order of other types of data. A third level ofrandomness may be introduced by data content shufflers 1930 (including,e.g., 1930 ₁, 1930 ₂, and/or 1930 ₃) as bit allocation within data froman individual data source, and may vary with compression methods.Depending on the subsequent encryption algorithm, shuffling at thislevel may have different granularities from randomly allocating bitstreams according to objects or groups or further down to the bytelevel. For example, data content shuffler 1930 ₁ may, for low-delay,low-complexity lossless encoded LPCM or logarithmic PCM data, randomlylocate the order of quantization segments and quantization indexes inbit stream, and/or may switch big-endian and little-endianrepresentation for every n bytes in the bit stream. As another example,data content shuffler 1930 ₂ may, for video sequence data, randomlylocate the order of object contents and encoded residuals, and/or mayswitch big-endian and little-endian representation for every n bytes inthe bit stream. Data content shuffler 1930 ₃ may, for other data,randomly locate the order of groups of data, and/or may switchbig-endian and little-endian representation for every n bytes in the bitstream.

As adding randomness in deep levels of bit streams may increase thecomputation complexity, and depending on the encryption method thatfollows, adding three or four levels of randomness may not be necessary.It is contemplated that bit-stream shuffler 220 may be configured toadjust a desired randomness level.

With reference back to FIG. 2, after bit stream shuffler 220 hasshuffled the bit stream, encrypter 225 may encrypt the bit stream. In anexemplary embodiment, the shuffling process may be tuned to correspondwith a particular encryption algorithm that is being used to minimizethe randomness level and computation complexity above a certain amount.After encryption, packetizer 230 may group the encrypted and encoded bitstream into packets for transmission to local unit 105 using specifictransmission protocols. Protocol packetization may be important forefficiency of bandwidth use, and therefore careful selection ofprotocol, packet size, network jitter buffer sitting, etc., may beimportant. It is contemplated that any encryption method and/or anypacketization method or protocol may be implemented. It is alsocontemplated that encypter 225 and decrypter 240 may be omitted. Inlocal unit 105, depacketizer 235 may reconstruct the encrypted, encodedbit-stream from the transmitted packets, decrypter 240 may decrypt thebit-stream, and bit-stream deshuffler may deshuffle the bit-stream.Decoders 250 may decode the compression encoding of various types ofdata 126, which then may be mixed back together by data mixer 255.

The disclosed real-time, low-delay, low-complexity lossless compressionmethod may be specifically used with target vehicle telemetry, but isnot necessarily limited to such applications. General losslesscompression algorithms often reach less than 3:1 compression ratio onaverage. It is contemplated that the disclosed systems and methods maybe able to achieve compression ratios of 5:1 or greater by utilizing thecharacteristics of data sources and correlations among different datasources, in order to remove higher order redundancy. Each compressionalgorithm of various types may be used independently or in variouscombinations. Multilevel randomness that is introduced into encodedbit-streams maintains a level of security by removing patterns thatresult from the compression algorithms.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the disclosed telemetrysystem. Other embodiments will be apparent to those skilled in the artfrom consideration of the specification and practice of the disclosedtelemetry system. It is intended that the specification and examples beconsidered as exemplary only, with a true scope being indicated by thefollowing claims and their equivalents.

What is claimed is:
 1. A method for transmitting data, comprising:receiving data from two or more data sources; selectively classifyingthe data into at least two input data streams, the at least two datastreams including a one dimensional data stream, called an encoded datastream, and an imagery data stream, which can include still images orvideo or both; and separately compressing the one dimensional datastream into a first compressed bit-stream and the imagery data streaminto a second compressed bit-stream; combining the first compressedbit-stream and the second compressed bit-stream into a packetizedbit-stream; encrypting the packetized bit-stream to generate encrypteddata packets prepared for transmission, wherein the one dimensional datastream is tiled into non-overlap segments using a state detectionalgorithm that categorizes the non-overlap segments into differentstates, wherein the different states include a constant level state, aperiodical change state, a slow variation state, a random noise state,and a transition state, and wherein corresponding encoders are used tocompress each of the categorized segments.
 2. The method of claim 1,wherein compressing the one-dimensional data stream comprises: using aprediction model to generate predicted one-dimensional data; determininga difference between the predicted one-dimensional data and theone-dimensional data; and encoding the difference with a compressionencoder.
 3. The method of claim 2, wherein: the one-dimensional datastream comprises logarithmic one-dimensional data, and datacorresponding to quantization segments of the one-dimensional datastream is encoded separate from data corresponding to quantizationindexes of the logarithmic one-dimensional data.
 4. The method of claim3, wherein: the data corresponding to quantization segments is encodedby using run-length coding; the data corresponding to quantizationindexes is encoded by generating predicted data corresponding toquantization indexes and encoding the difference between the predicteddata and the data corresponding to the quantization indexes using acompression encoder.
 5. The method of claim 1, wherein the imagery datastream comprises video and compressing the imagery data streamcomprises: extracting offset information from the video; compressing thevideo based on data within one frame of the video using an intra-frameprediction method; and compressing the video based on data of severalframes of the video using an inter-frame prediction method.
 6. Themethod of claim 1, wherein the combining the first compressed bit-streamand the second compressed bit-stream comprises shuffling the first andsecond compressed bit-streams into a shuffled bit-stream, whereinshuffling the first compressed bit-stream and the second compressedbit-stream comprises at least one of: randomly allocating data ofdifferent classifications in the shuffled bit-stream; randomlyallocating an order of data from different data sources in the shuffledbit-stream; or randomly allocating bit allocation within data from eachspecific data source in the shuffled bit-stream.
 7. A data transmissionsystem, comprising: a local unit comprising a receiver; and a remoteunit, the remote unit comprising transducers, a processor, and atransmitter; the transducers are configured to generate at least twodata streams; the processor is configured to compress the data by:selectively classifying the data streams from the transducers intoclassifications including at least a one-dimensional data stream and animagery data stream, and separately compressing the one dimensional datastream into a first compressed bit-stream and the imagery data streaminto a second compressed bit-stream; and the transmitter is configuredto transmit the first compressed bit-stream and the second compressedbit-stream to the local unit, wherein the processor is furtherconfigured to: tile the one dimensional data stream into non-overlapsegments using a state detection algorithm that categorizes thenon-overlap segments into different states, wherein the different statesinclude a constant level state, a periodical change state, a slowvariation state, a random noise state, and a transition state, andcompress each of the categorized segments using corresponding encoders.8. The system of claim 7, wherein compressing the one-dimensional datastream comprises: using a prediction model to generate predictedone-dimensional data; determining a difference between the predictedone-dimensional data and the one-dimensional data; and encoding thedifference with a compression encoder.
 9. The system of claim 8,wherein: the encoded data stream comprises logarithmic one-dimensionaldata, and data corresponding to quantization segments of theone-dimensional data is encoded separate from data corresponding toquantization indexes of the logarithmic one-dimensional data.
 10. Thesystem of claim 9, wherein: the data corresponding to quantizationsegments is encoded by using run-length coding; the data correspondingto quantization indexes is encoded by generating predicted datacorresponding to quantization indexes and encoding the differencebetween the predicted data and the data corresponding to thequantization indexes using a compression encoder.
 11. The method ofclaim 1, wherein separately compressing the one-dimensional data streamand the imagery data stream comprises compressing the one-dimensionaldata stream using a frame based waveform compression method.
 12. Thesystem of claim 7, wherein separately compressing the one-dimensionaldata stream and the imagery data stream comprises compressing theone-dimensional data stream using a frame based waveform compressionmethod.
 13. The method of claim 1, wherein: the corresponding encodersuse one or more of the following different compression methods for thedifferent states: a constant compressor for constant states, a noiseencoder for random noise states, and an adaptive waveform encoder forrest types of states.
 14. The system of claim 7, wherein the processoris further configured to: compute first order derivatives of each of thenon-overlap segments of the at least two data streams using the statedetection algorithm, wherein the constant level state is determined whena majority of the first order derivatives of each of the non-overlapsegments are zeros, and calculate cross-correlation coefficients anddefine the periodical change state when a first peak is above athreshold.
 15. The system of claim 13, wherein: the adaptive waveformencoder creates prediction samples using corresponding prediction modelsbased on the different states, computes prediction residuals, andencodes the prediction residuals using entropy coding methods.
 16. Thesystem of claim 7, further comprising: a random noise encoder,implemented by the processor, that first calculates the mean of firstorder derivatives, calculates the dynamic range with the mean removal,and encodes the mean and corresponding zero mean first order derivativesusing minimum number of bit requirement.
 17. The system of claim 7,further comprising: a constant value compressor, implemented by theprocessor, that encodes the constant value if all the samples of firstorder derivatives are zeros and encodes the constant values, the numberof non-zeros, the pairs of position, and values of the non-zeros. 18.The system of claim 15, wherein: the adaptive waveform encoder computesa total number of bits required for a skeleton and residualrepresentation, 2^(nd), 4^(th) and 6^(th) order auto regressionprediction models with corresponding residual representation, andchooses the representation with a minimum number of bits requirement.